CIKM 2016. Our first dataset is the publicly released dataset from track two of the CIKM Cup 2016.https://competitions.codalab.org/competitions/11161
This dataset contains six months of anonymized users' search logs including query and product description tokens, clicks, views and purchase records on an e-commerce search engine from January 1st, 2016 to June 1st, 2016. The dataset contains additional product metadata such as product categories, description and price. In the original split of the data, the last query of each session is marked as test sample, so we do not have user interaction signals for these queries. In addition to search sessions that come with queries, this dataset also contains browsing logs that are query-less. We ignore this part of the data in our experiments, since we are interested in query/document matching based on text, and we study its impact in a SERP re-ranking task. As a consequence, since the results achieved here are only based on text matching on query-full queries, they are not comparable to studies in which the whole dataset is used to improve the ranking such as [1]}, [2]}.
<TABLE>